262 research outputs found
Learning Resource Allocation Policy: Vertex-GNN or Edge-GNN?
Graph neural networks (GNNs) update the hidden representations of vertices
(called Vertex-GNNs) or hidden representations of edges (called Edge-GNNs) by
processing and pooling the information of neighboring vertices and edges and
combining to incorporate graph topology. When learning resource allocation
policies, GNNs cannot perform well if their expressive power are weak, i.e., if
they cannot differentiate all input features such as channel matrices. In this
paper, we analyze the expressive power of the Vertex-GNNs and Edge-GNNs for
learning three representative wireless policies: link scheduling, power
control, and precoding policies. We find that the expressive power of the GNNs
depend on the linearity and output dimensions of the processing and combination
functions. When linear processors are used, the Vertex-GNNs cannot
differentiate all channel matrices due to the loss of channel information,
while the Edge-GNNs can. When learning the precoding policy, even the
Vertex-GNNs with non-linear processors may not be with strong expressive
ability due to the dimension compression. We proceed to provide necessary
conditions for the GNNs to well learn the precoding policy. Simulation results
validate the analyses and show that the Edge-GNNs can achieve the same
performance as the Vertex-GNNs with much lower training and inference time
Multidimensional Graph Neural Networks for Wireless Communications
Graph neural networks (GNNs) have been shown promising in improving the
efficiency of learning communication policies by leveraging their permutation
properties. Nonetheless, existing works design GNNs only for specific wireless
policies, lacking a systematical approach for modeling graph and selecting
structure. Based on the observation that the mismatched permutation property
from the policies and the information loss during the update of hidden
representations have large impact on the learning performance and efficiency,
in this paper we propose a unified framework to learn permutable wireless
policies with multidimensional GNNs. To avoid the information loss, the GNNs
update the hidden representations of hyper-edges. To exploit all possible
permutations of a policy, we provide a method to identify vertices in a graph.
We also investigate the permutability of wireless channels that affects the
sample efficiency, and show how to trade off the training, inference, and
designing complexities of GNNs. We take precoding in different systems as
examples to demonstrate how to apply the framework. Simulation results show
that the proposed GNNs can achieve close performance to numerical algorithms,
and require much fewer training samples and trainable parameters to achieve the
same learning performance as the commonly used convolutional neural networks
Understanding the Performance of Learning Precoding Policy with GNN and CNNs
Learning-based precoding has been shown able to be implemented in real-time,
jointly optimized with channel acquisition, and robust to imperfect channels.
Yet previous works rarely explain the design choices and learning performance,
and existing methods either suffer from high training complexity or depend on
problem-specific models. In this paper, we address these issues by analyzing
the properties of precoding policy and inductive biases of neural networks,
noticing that the learning performance can be decomposed into approximation and
estimation errors where the former is related to the smoothness of the policy
and both depend on the inductive biases of neural networks. To this end, we
introduce a graph neural network (GNN) to learn precoding policy and analyze
its connection with the commonly used convolutional neural networks (CNNs). By
taking a sum rate maximization precoding policy as an example, we explain why
the learned precoding policy performs well in the low signal-to-noise ratio
regime, in spatially uncorrelated channels, and when the number of users is
much fewer than the number of antennas, as well as why GNN is with higher
learning efficiency than CNNs. Extensive simulations validate our analyses and
evaluate the generalization ability of the GNN
Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks
Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Co-Design Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates (1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and (2) an Energy-Efficient, Multi-Level Computing Architecture Specifically Designed to Leverage the Multi-Resolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Physical Beam and Simulations of a Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency. © 2010 ACM
Cyber-Physical Codesign of Distributed Structural Health Monitoring with Wireless Sensor Networks
Our Deteriorating Civil Infrastructure Faces the Critical Challenge of Long-Term Structural Health Monitoring for Damage Detection and Localization. in Contrast to Existing Research that Often Separates the Designs of Wireless Sensor Networks and Structural Engineering Algorithms, This Paper Proposes a Cyber-Physical Codesign Approach to Structural Health Monitoring based on Wireless Sensor Networks. Our Approach Closely Integrates 1) Flexibility-Based Damage Localization Methods that Allow a Tradeoff between the Number of Sensors and the Resolution of Damage Localization, and 2) an Energy-Efficient, Multilevel Computing Architecture Specifically Designed to Leverage the Multiresolution Feature of the Flexibility-Based Approach. the Proposed Approach Has Been Implemented on the Intel Imote2 Platform. Experiments on a Simulated Truss Structure and a Real Full-Scale Truss Structure Demonstrate the System\u27s Efficacy in Damage Localization and Energy Efficiency
CARE: A Large Scale CT Image Dataset and Clinical Applicable Benchmark Model for Rectal Cancer Segmentation
Rectal cancer segmentation of CT image plays a crucial role in timely
clinical diagnosis, radiotherapy treatment, and follow-up. Although current
segmentation methods have shown promise in delineating cancerous tissues, they
still encounter challenges in achieving high segmentation precision. These
obstacles arise from the intricate anatomical structures of the rectum and the
difficulties in performing differential diagnosis of rectal cancer.
Additionally, a major obstacle is the lack of a large-scale, finely annotated
CT image dataset for rectal cancer segmentation. To address these issues, this
work introduces a novel large scale rectal cancer CT image dataset CARE with
pixel-level annotations for both normal and cancerous rectum, which serves as a
valuable resource for algorithm research and clinical application development.
Moreover, we propose a novel medical cancer lesion segmentation benchmark model
named U-SAM. The model is specifically designed to tackle the challenges posed
by the intricate anatomical structures of abdominal organs by incorporating
prompt information. U-SAM contains three key components: promptable information
(e.g., points) to aid in target area localization, a convolution module for
capturing low-level lesion details, and skip-connections to preserve and
recover spatial information during the encoding-decoding process. To evaluate
the effectiveness of U-SAM, we systematically compare its performance with
several popular segmentation methods on the CARE dataset. The generalization of
the model is further verified on the WORD dataset. Extensive experiments
demonstrate that the proposed U-SAM outperforms state-of-the-art methods on
these two datasets. These experiments can serve as the baseline for future
research and clinical application development.Comment: 8 page
- …